README.md

---
license: cc-by-4.0
viewer: false
---
# Dataset summary
This dataset is designed to assist in predicting a customer's propensity to purchase various products within a month following the reporting date. The dataset includes anonymized historical data on transaction activity, dialog embeddings, and geo-activity for some bank clients over 12 months.

Reduced dataset version is avaliable as [MBD-mini](https://huggingface.co/datasets/ai-lab/MBD-mini). The mini MBD dataset contains a reduced subset of the data, making it easier and faster to work with during the development and testing phases. It includes a smaller number of clients and a shorter time span but maintains the same structure and features as the full dataset. MBD-mini has data based on 10% of unique clients listed in MBD.

# Data
The dataset consists of anonymized historical data, which contains the following information for some of the Bank's clients over 12 months:
- transaction activity (transactions) Details about past transactions including amounts, types, and dates;
- dialog embeddings (dialogs) Embeddings from customer interactions, which capture semantic information from dialogues;
- geo-activity (geostream) Location-based data representing clients' geographic activity patterns.

Objective: To predict for each user the taking/not taking of each of the four products within a month after the reporting date, historical data for them is in targets

The dataset is divided into 5 folds based on client_split (which consist of an equal number of unique clients) for cross-validation purposes.

```
client_split Desc: Splitting clients into folds
|-- client_id: str Desc: Client id
|-- fold: int

detail
|-- dialog Desc: Dialogue embeddings
    |-- client_id: str Desc: Client id
    |-- event_time: timestamp Desc: Dialog's date
    |--embedding: array float Desc: Dialog's embeddings
    |-- fold: int
|-- geo Desc: Geo activity
    |-- client_id: str Desc: Client id
    |-- event_time: timestamp Desc: Event datetime
    |-- fold: int
    |-- geohash_4: int Desc: Geohash level 4
    |-- geohash_5: int Desc: Geohash level 5
    |-- geohash_6: int Desc: Geohash level 6
|-- trx Desc: Transactional activity
    |-- client_id: str Desc: Client id
    |-- event_time: timestamp Desc: Transaction's date
    |-- amount: float Desc: Transaction's amount
    |-- fold: int
    |-- event_type: int Desc: Transaction's type
    |-- event_subtype: int Desc: Clarifying the transaction type
    |-- currency: int Desc: Currency
    |-- src_type11: int Desc: Feature 1 for sender
    |-- src_type12: int Desc: Clarifying feature 1 for sender
    |-- dst_type11: int Desc: Feature 1 for contractor
    |-- dst_type12: int Desc: Clarifying feature 1 for contractor 
    |-- src_type21: int Desc: Feature 2 for sender
    |-- src_type22: int Desc: Clarifying feature 2 for sender
    |-- src_type31: int Desc: Feature 3 for sender
    |-- src_type32: int Desc: Clarifying feature 3 for sender

ptls Desc: Data is similar with detail but in pytorch-lifestream format https://github.com/dllllb/pytorch-lifestream
|-- dialog Desc: Dialogue embeddings
    |-- client_id: str Desc: Client id
    |-- event_time: Array[timestamp] Desc: Dialog's date
    |-- embedding: Array[float] Desc: Dialog's embedding
    |-- fold: int
|-- geo Desc: Geo activity
    |-- client_id: str Desc: Client id
    |-- event_time: Array[timestamp] Desc: Event datetime
    |-- fold: int
    |-- geohash_4: Array[int] Desc: Geohash level 4
    |-- geohash_5: Array[int] Desc: Geohash level 5
    |-- geohash_6: Array[int] Desc: Geohash level 6
|-- trx Desc: Transactional activity
    |-- client_id: str Desc: Client id
    |-- event_time: Array[timestamp] Desc: Transaction's date
    |-- amount: Array[float] Desc: Transaction's amount
    |-- fold: int
    |-- event_type: Array[int] Desc: Transaction's type
    |-- event_subtype: Array[int] Desc: Clarifying the transaction type
    |-- currency: Array[int] Desc: Currency
    |-- src_type11: Array[int] Desc: Feature 1 for sender
    |-- src_type12: Array[int] Desc: Clarifying feature 1 for sender
    |-- dst_type11: Array[int] Desc: Feature 1 for contractor
    |-- dst_type12: Array[int] Desc: Clarifying feature 1 for contractor 
    |-- src_type21: Array[int] Desc: Feature 2 for sender
    |-- src_type22: Array[int] Desc: Clarifying feature 2 for sender
    |-- src_type31: Array[int] Desc: Feature 3 for sender
    |-- src_type32: Array[int] Desc: Clarifying feature 3 for sender

targets
|-- mon: str  Desc: Reporting month
|-- target_1: int Desc: Mark of product issuance in the first reporting month
|-- target_2: int Desc: Mark of product issuance in the second reporting month
|-- target_3: int Desc: Mark of product issuance in the third reporting month
|-- target_4: int Desc: Mark of product issuance in the fourth reporting month
|-- trans_count: int Desc: Number of transactions
|-- diff_trans_date: int Desc: Time difference between transactions
|-- client_id: str Desc: Client id
|-- fold: int
```
 
# Load dataset

## Download a single file
Download a single file with datasets
```python
from datasets import load_dataset

dataset = load_dataset("ai-lab/MBD", data_files='client_split.tar.gz')
```

Download a single file with huggingface_hub
```python
from huggingface_hub import hf_hub_download

hf_hub_download(repo_id="ai-lab/MBD", filename="client_split.tar.gz", repo_type="dataset")

# By default dataset is saved in '~/.cache/huggingface/hub/datasets--ai-lab--MBD/snapshots/<hash>/'
# To overwrite this behavior try to use local_dir 

```
## Download entire repository 
Download entire repository with datasets
```python
from datasets import load_dataset

dataset = load_dataset("ai-lab/MBD")
```

Download entire repository with huggingface_hub
```python
from huggingface_hub import snapshot_download

snapshot_download(repo_id="ai-lab/MBD")

# By default dataset is saved in '~/.cache/huggingface/hub/datasets--ai-lab--MBD/snapshots/<hash>/'
# To overwrite this behavior try to use local_dir 
```

# Citation
```
TBD
```